{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from shap_e.models.download import load_model\n",
    "from shap_e.util.data_util import load_or_create_multimodal_batch\n",
    "from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xm = load_model('transmitter', device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = \"example_data/cactus/object.obj\"\n",
    "\n",
    "# This may take a few minutes, since it requires rendering the model twice\n",
    "# in two different modes.\n",
    "batch = load_or_create_multimodal_batch(\n",
    "    device,\n",
    "    model_path=model_path,\n",
    "    mv_light_mode=\"basic\",\n",
    "    mv_image_size=256,\n",
    "    cache_dir=\"example_data/cactus/cached\",\n",
    "    verbose=True, # this will show Blender output during renders\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    latent = xm.encoder.encode_to_bottleneck(batch)\n",
    "\n",
    "    render_mode = 'stf' # you can change this to 'nerf'\n",
    "    size = 128 # recommended that you lower resolution when using nerf\n",
    "\n",
    "    cameras = create_pan_cameras(size, device)\n",
    "    images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
    "    display(gif_widget(images))"
   ]
  }
 ],
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